{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 导入必要的包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import glob\n",
    "import numpy as np\n",
    "import math\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.SURF\n",
    "    特征检测的视觉不变性是一个非常重要的概念。 但是要解决尺度不变性问题，难度相当大。 为解决这一问题，计算机视觉界引入了尺度不变特征的概念。 它的理念是， 不仅在任何尺度下拍摄的物体都能检测到一致的关键点，而且每个被检测的特征点都对应一个尺度因子。 理想情况下，对于两幅图像中不同尺度的的同一个物体点， 计算得到的两个尺度因子之间的比率应该等于图像尺度的比率。近几年， 人们提出了多种尺度不变特征，本节介绍其中的一种：SURF特征。 SURF全称为“加速稳健特征”（Speeded Up Robust Feature），我们将会看到，它们不仅是尺度不变特征，而且是具有较高计算效率的特征。\n",
    "    Surf 是sift的变种，更高效"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " hessianThreshold代表Hessian矩阵行列式所计算出的曲率强度，数值越高，代表区分匹配点的要求越高，一般推荐1000-2500之间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def SURF_detect(img1, img2):\n",
    "    ##灰度图转换\n",
    "    gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)\n",
    "    gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\n",
    "    \n",
    "    # 使用SURF检测角点\n",
    "    minThreashold = 1000  # hession 矩阵阈值，在这里调整精度，值越大点越少，越精准\n",
    "    surf = cv2.xfeatures2d_SURF.create(minThreashold)\n",
    "    \n",
    "    ##keypoints:特征点  descriptor：描述符\n",
    "    keypoints, descriptor = surf.detectAndCompute(gray1, None)\n",
    "    keypoints2, descriptor2 = surf.detectAndCompute(gray2, None)\n",
    "    \n",
    "    # 定义FLANN匹配器\n",
    "    # 使用KNN算法匹配\n",
    "    bfMatcher = cv2.FlannBasedMatcher()\n",
    "    matches = bfMatcher.knnMatch(descriptor, descriptor2, k=2)\n",
    "    \n",
    "    # 去除错误匹配\n",
    "    #Lowe's algorithm,获取优秀匹配点\n",
    "    good = [[m] for m, n in matches if m.distance < 0.5 * n.distance]\n",
    "    img3 = cv2.drawMatchesKnn(img1, keypoints, img2, keypoints2, good, None, flags=2)\n",
    "\n",
    "    img3 = cv2.resize(img3, (1920, 540))\n",
    "\n",
    "    plt.imshow(img3)\n",
    "    cv2.imwrite(\"surf_matching.jpg\", img3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.SIFT\n",
    "    SURF算法是SIFT算法的加速版， 而SIFT（尺度不变特征转换， ScaleInvariant Feature Transform） 是另一种著名的尺度不变特征检测法。我们知道，SURF相对于SIFT而言，特征点检测的速度有着极大的提升，所以在一些实时视频流物体匹配上有着很强的应用。而SIFT因为其巨大的特征计算量而使得特征点提取的过程异常花费时间，所以在一些注重速度的场合难有应用场景。但是SIFT相对于SURF的优点就是，由于SIFT基于浮点内核计算特征点，因此通常认为， SIFT算法检测的特征在空间和尺度上定位更加精确，所以在要求匹配极度精准且不考虑匹配速度的场合可以考虑使用SIFT算法。\n",
    "\n",
    "    SIFT特征检测的代码我们仅需要对上面的SURF代码作出一丁点修改即可。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sift(img1, img2):\n",
    "    gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)\n",
    "    gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "    maxcorners = 1000\n",
    "\n",
    "    sift = cv2.xfeatures2d_SIFT.create(nfeatures=maxcorners)\n",
    "    kp1, descriptors1 = sift.detectAndCompute(gray1,None)\n",
    "    kp2, descriptors2 = sift.detectAndCompute(gray2,None)\n",
    "\n",
    "    bfMatcher = cv2.FlannBasedMatcher()\n",
    "    matches = bfMatcher.knnMatch(descriptors1, descriptors2, k=2)\n",
    "\n",
    "    good = [[m] for m, n in matches if m.distance < 0.5 * n.distance]\n",
    "    img3 = cv2.drawMatchesKnn(img1, kp1, img2, kp2, good, None, flags=2)\n",
    "\n",
    "    img3 = cv2.resize(img3, (1920, 540))\n",
    "\n",
    "    plt.imshow(img3)\n",
    "    cv2.imwrite(\"sift_matching.jpg\", img3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.ORB\n",
    "    ORB是ORiented Brief的简称，是brief算法的改进版。ORB算法比SIFT算法快100倍，比SURF算法快10倍。在计算机视觉领域有种说法，ORB算法的综合性能在各种测评里较其他特征提取算法是最好的。\n",
    "\n",
    "    ORB算法是brief算法的改进，那么我们先说一下brief算法有什么去缺点。\n",
    "    BRIEF的优点在于其速度，其缺点是：\n",
    "\n",
    "       不具备旋转不变性\n",
    "       对噪声敏感\n",
    "       不具备尺度不变性\n",
    "       \n",
    "    而ORB算法就是试图解决上述缺点中1和2提出的一种新概念。值得注意的是，ORB没有解决尺度不变性。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ORB_detect(img1, img2):\n",
    "    gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)\n",
    "    gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "    nkeypoint = 100  # 算法在图片中找到匹配点的对数\n",
    "\n",
    "    orb = cv2.ORB.create(nkeypoint)\n",
    "\n",
    "    kp1, descriptors1 = orb.detectAndCompute(gray1, None)\n",
    "    kp2, descriptors2 = orb.detectAndCompute(gray2, None)\n",
    "\n",
    "    bf = cv2.DescriptorMatcher.create('BruteForce')\n",
    "    matches = bf.match(descriptors1, descriptors2)\n",
    "\n",
    "\n",
    "    img3 = cv2.drawMatches(img1, kp1, img2, kp2, matches, None, flags=2)\n",
    "\n",
    "    img3 = cv2.resize(img3, (1920, 540))\n",
    "\n",
    "    plt.imshow(img3)\n",
    "    cv2.imwrite(\"orb_matching.jpg\", img3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "加载图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "img1 = cv2.imread('./imgs2/1.jpg')\n",
    "img2 = cv2.imread('./imgs2/2.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "测试三种不同的角点检测和匹配算法，其中surf和sift的准确率都不错"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "error",
     "evalue": "OpenCV(3.4.6) D:\\Build\\OpenCV\\opencv_contrib-3.4.6\\modules\\xfeatures2d\\src\\surf.cpp:1029: error: (-213:The function/feature is not implemented) This algorithm is patented and is excluded in this configuration; Set OPENCV_ENABLE_NONFREE CMake option and rebuild the library in function 'cv::xfeatures2d::SURF::create'\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31merror\u001b[0m                                     Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-a4929098b2ea>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mSURF_detect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-2-00cfe377f0b7>\u001b[0m in \u001b[0;36mSURF_detect\u001b[1;34m(img1, img2)\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[1;31m# 使用SURF检测角点\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mminThreashold\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1000\u001b[0m  \u001b[1;31m# hession 矩阵阈值，在这里调整精度，值越大点越少，越精准\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[0msurf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxfeatures2d_SURF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mminThreashold\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[1;31m##keypoints:特征点  descriptor：描述符\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31merror\u001b[0m: OpenCV(3.4.6) D:\\Build\\OpenCV\\opencv_contrib-3.4.6\\modules\\xfeatures2d\\src\\surf.cpp:1029: error: (-213:The function/feature is not implemented) This algorithm is patented and is excluded in this configuration; Set OPENCV_ENABLE_NONFREE CMake option and rebuild the library in function 'cv::xfeatures2d::SURF::create'\n"
     ]
    }
   ],
   "source": [
    "SURF_detect(img1, img2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ORB_detect(img1, img2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "error",
     "evalue": "OpenCV(3.4.5) d:\\build\\opencv\\opencv_contrib-3.4.5\\modules\\xfeatures2d\\src\\sift.cpp:1207: error: (-213:The function/feature is not implemented) This algorithm is patented and is excluded in this configuration; Set OPENCV_ENABLE_NONFREE CMake option and rebuild the library in function 'cv::xfeatures2d::SIFT::create'\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31merror\u001b[0m                                     Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-14-60bddfdad2aa>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msift\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-7-97cff708d19f>\u001b[0m in \u001b[0;36msift\u001b[1;34m(img1, img2)\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mmaxcorners\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1000\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m     \u001b[0msift\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxfeatures2d_SIFT\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnfeatures\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmaxcorners\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m     \u001b[0mkp1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdescriptors1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msift\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdetectAndCompute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgray1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[0mkp2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdescriptors2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msift\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdetectAndCompute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgray2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31merror\u001b[0m: OpenCV(3.4.5) d:\\build\\opencv\\opencv_contrib-3.4.5\\modules\\xfeatures2d\\src\\sift.cpp:1207: error: (-213:The function/feature is not implemented) This algorithm is patented and is excluded in this configuration; Set OPENCV_ENABLE_NONFREE CMake option and rebuild the library in function 'cv::xfeatures2d::SIFT::create'\n"
     ]
    }
   ],
   "source": [
    " sift(img1, img2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "没找到python3.7的opencv3.4.1以下的版本"
   ]
  }
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